Impulse of Dividend Payment Decision: Evidence from Pharmaceutical Industry in Bangladesh
Bibliographic record
Abstract
The dividend is the reward of shareholders of an organization in exchange for time and risk. For maximizing shareholder’s wealth, optimum dividend payout ratio is essential. The prime objective of this paper is to identify impulse of dividend payment decision of listed pharmaceutical companies in Dhaka Stock Exchange of Bangladesh. Dividend payment decision is the dependent variable and profitability, firm’s size, financial leverage, growth, and agency costs are taken as explanatory variables in this study. Collected secondary data are analyzed by econometrics software Eviews 8 through least square method. Formulated multiple regression models show value of R-square (R2) is 0.604817. R-square (R2) value indicates explanatory variables explain 60.48% variation of the dependent variable. The study also reveals that profitability and agency cost positively influence the dividend payment decision and firm’s size, financial leverage, growth negatively impact on the dividend payment decision of selected pharmaceutical companies. Among explanatory variables, profitability is not statistically significant at 5% significant level whereas firm’s size, financial leverage, growth and agency cost are found statistically significant at 5% significant level. So this paper finds that listed pharmaceutical companies in Dhaka Stock Exchange must consider firm’s size, financial leverage, growth and agency cost in their dividend payment decision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".